Psychiatry Portal: Calculating Results

Absolute Risk 

The likelihood of an event happening under specific conditions (usually a “1/10 chance” or “10% chance”) 

• # of events (good or bad) in treated or control groups, divided by the number of people in that group

Relative Risk (RR) 

The likelihood of an event occurring in a group of people compared to another group with different behaviors, physical conditions, or environments (usually a % increase/decrease compared to groups)

• RR = ART/ARC (# events in treatment/# events in control)

Relative Risk ratio 

The ratio of the probability of an event occurring in the exposed group versus the probability of the event occurring in the non-exposed group

Absolute Risk Reduction (ARR) – “risk difference” 

Shows exact difference from baseline, how much lower the modified risk is from the start risk in absolute terms

• ARR = ARC – ART (# events in control - # events in treatment)

Relative Risk Reduction (RRR) – relative number 

Tells us how much lower the modified risk is than the starting risk as a proportion. Can look very impressive for small differences.

• Calculated RRR = 1-RR  OR (ARC – ART)/ARC

Hazard Ratio

HR is a comparison between the probability of events in a treatment group compared to the probability of events in a control group

• HR >1 means treatment group healed faster or had a slower time to event
• HR = 1 means that both groups are experiencing equal number of events at any point in time
  • Hazard ratio of 3 - 3x the number of events are seen in treatment group at any point in time; the treatment will cause the patient to progress three times as fast as patients in the control group
  • Hazard ratio of 0.333 – HR in treatment group is 1/3 of that in the control group
• Similar (but not the same as) relative risk ratio; RRR is cumulative over the entire period of the study; HR is at any particular point in time

Absolute Risk Reduction, Relative Risk Reduction, Relative Risk resources

• How to calculate a relative risk. Terry Shaneyfelt video (3 min)
• Basic Statistics for Clinicians: Assessing the effects of treatment: measures of association CMAJ. 1995 Feb 1;152(3):351-7. Erratum in: Can Med Assoc J 1995 Mar 15;152(6):813.

Odds Ratio

Compares odds of an exposure in cases to odds of exposure in controls

• Is also a measure of outcome vs exposure 

• Reasonable approximation of relative risk when the disease/outcome being studied is relatively rare (<10%) in people who have  not been exposed. 
• When used correctly, they have a similar meaning as relative risk
  • OR > 1, increased likelihood of the outcome
  • OR < 1 , decreased likelihood of the outcome
•  Odds Ratios can be calculated for case control studies (in which the prevalence of disease is for exposed vs unexposed population is not known)

Odds Ratios resources

• Gordon Guyatt explaining odds ratios video (21 min)
• How to calculate an odds ratio Terry Shaneyfelt video (3 min)
• Odds ratios Philip Sedgwick. BMJ 2010;341:c4414

Number needed to Treat (NNT)

The number of patients you need to treat to prevent one additional bad outcome (death, stroke, heart attack, etc.)

• NNT = 1/ARR

  • ARR = CER – EER (control event rate – experimental event rate)
• NNT are always rounded up to nearest whole number and accompanied as standard by 95% CI
  • Ex – if a drug reduces risk of a bad outcome from 50%

Sensitivity and Specificity

• Describe the performance of test in a population
• The sensitivity and specificity of a test are independent of the prevalence of the disease in the population for which it is being used.

Positive predictive value, Negative predictive value

PPV = # of true positives / (# of true positives + # false positives)  (top of 2x2 table)

NPV = true negative rate / (true negative rate + false negative rate)*100  (bottom of 2x2 table)

• The probability that the disease is truly present- PPV - or absent- NPV, given a positive (PPV) or negative (NPV) test result
• The positive and negative predictive values  for a test VARY according to the prevalence of disease in a population
  • Baye’s theorem

Sensitivity and PPV

• Sensitivity - What % of people with disease does test correctly identify?Positive In Disease
• PPV - If the test result is positive, what is the probability of disease?

Specificity and NPV

• Specificity - What % of people without disease does test ID correctly? Negative In Health
• NPV - If the test result is negative, what is the probability of NO disease?

Sensitivity, Specificity and Likelihood ratios resources

• Sensitivity & specificity - H. Gilbert Welch video (8 min)
• How to calculate sensitivity - Terry Shaneyfelt video (4 min)
• How to calculate specificity - Terry Shaneyfelt video (3 min)

Assess the potential utility of a particular diagnostic test & looks at how likely it is that a patient has a disease or condition

• LRs are basically a ratio of the probability that a test result is correct to the probability that the test result is incorrect.
  • LR+ =  sensitivity / 1- specificity  = TP/FP
  • LR-  = 1- sensitivity / specificity =  FN/TN

Understanding the numbers:

• LR >1 means that the test result is associated with the PRESENCE of disease.  
• LR <1 (between 0 and 1)  means that the test result is associated with the ABSENCE of disease.
• LR = 1 means that the test result does not affect the likelihood of disease (i.e.worthless clinically!)
  • LRs are ratios derived from sensitivity and specificity (which are independent of disease prevalence). 
  • Multiply pre test odds by the likelihood ratio to get post test odds
  • Convert post test odds to post test probability

Characteristics of LR

• They are not dependent on the prevalence of the target disease in a population, because they are calculated from sensitivity and specificity
• They allow the clinician to calculate the probability of disease, via calculating pre and post test ODDS, for a given test result. 
  • PPV and NPV do also, but they are prevalence dependent

LR resources

• What are likelihood ratios and how are they used Terry Shaneyfelt video (10 min)

Forest Plots

• How to interpret a forest plot. Terry Shaneyfelt video (5 min)
• Interpreting and understanding meta-analysis graphs--a practical guide. Aust Fam Physician. 2006 Aug;35(8):635-8. PubMed PMID: 16894442

​

Types of Heterogeneity:

• Clinical: Differences in participants, interventions or outcomes
• Methodological: Differences in study design, risk of bias
• Statistical: Variation in intervention effects or results

Measuring Heterogeneity

• Look at it with the eyeball test on a forest plot (you can draw a line through all the points)
• OR use χ² test or I²
  • χ² assumes null hypothesis that all studies are homogenous – gives us a p-value to test. If p-value is low, we can reject the hypothesis, and heterogeneity is present

I²

measures extent of heterogeneity. Low is good, high is bad for numbers. >50% is a bad combination of tests.

• 0% to 40%: might not be important
• 30% to 60%: moderate heterogeneity
• 50% to 90%: substantial heterogeneity
• 75% to 100%: considerable heterogeneity

Resources:

What is heterogeneity and is it important? BMJ 2007; 334 :94. Fletcher, J.

Deeks JJ, Higgins JPT, Altman DG (editors). Chapter 9: Analysing data and undertaking meta-analyses. In: Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. Available from www.cochrane-handbook.org.

What is Heterogeneity and why does it matter? Students 4 Best Evidence

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P-Value

Probability that the null hypotheses gives for a specific experimental result to happen; goal of experiments is to disprove the null hypothesis

• Low (<0.05) – null hypothesis is unlikely, and has statistical significance
• Low p-value means a higher chance of hypothesis being true
• Post-hoc tests – probabilities in post-hoc tests are CUMULATIVE – so probability of each test must equal α / the number of comparisons to preserve the overall significance level. You can’t just combine them and look for a p =0.05; should also adjust confidence intervals if that’s the only piece given

95% confidence interval is a range of values that you can be 95% certain contains the true mean of the population.

Confidence Intervals

• Confidence intervals Terry Shaneyfelt video (5 min)

• Basic Statistics for Clinicians: Interpreting study results: confidence intervals CMAJ. 1995 Jan 15;152(2):169-73.

• more videos by Terry Shaneyfelt, M.D.
• more videos by Allan Shaughnessy
• Stating the meaning of effect size measures in plain English
• Endgames statistical series in BMJ by Philip Sedgwic
• Students4BestEvidence